1. Field of the Invention
The present invention relates to systems and methods for providing information regarding media programs to users, and in particular to a system and method for recommending media programs to users based on correlated user feedback.
2. Description of the Related Art
The dissemination and playback of media programs has undergone substantial changes in the past decade. Previously, media programs (which may include audio, video, or both) were disseminated either by analog broadcast (conventional, satellite, or cable) or by dissemination of films to movie theaters.
These traditional dissemination and playback means remain in use after the advent of digital technology. However, digital technologies have had a profound effect on the dissemination and playback of media programs.
First, digital technology permitted the use of digital video recorders (DVRs). DVRs, while similar in function to standard analog video cassette recorders (VCRs), provide a number of additional useful functions including live pause, the ability to record one program while playing back another, and the integration of the electronic program guides with DVR functionality (so that the recordation of media programs could be scheduled far in advance).
Second, digital technology also permitted the dissemination and playback of media programs via the Internet, and with improved signal processing and more and more households with high-speed Internet access (e.g. DSL, fiber, and/or satellite). These methods of dissemination and playback have become competitive with traditional means. Dissemination of media programs via the Internet may occur either by simple downloading, progressive downloading or streaming.
For progressive download, a media file having the media program is downloaded via the Internet using dial-up, DSL, ADSL, cable, T1, or other high speed connection. Such downloading is typically performed by a web server via the Internet.
Simple downloading downloads the bytes of the media file in any convenient order, while progressive download downloads bytes at the beginning of a file and continues downloading the file sequentially and consecutively until the last byte. At any particular time during progressive downloading, portions of the file may not be immediately available for playback. In some situations, the entire file must be downloaded first before a media player can start playback. In other progressive download situations, media players are able to start playback once enough of the beginning of the file has downloaded, however, the media player must download enough information to support some form of playback before playback can occur. Playback of progressively downloaded media files is often delayed by slow Internet connections and is also often choppy and/or contains a high likelihood of stopping after only a few seconds. Once a progressively downloaded media program has been completely downloaded, it may be stored on the end-user computer for later use.
One of the disadvantages of a progressive downloading is that the entity transmitting the data (the web server) simply pushes the data to the client as fast as possible. It may appear to be “streaming” the video because the progressive download capability of many media players allows playback as soon as an adequate amount of data has been downloaded. However, the user cannot fast-forward to the end of the file until the entire file has been delivered by the web server. Another disadvantage with progressive downloading is that the web server does not make allowances for the data rate of the video file. Hence, if the network bandwidth is lower than the data rate required by the video file, the user must wait a period of time before playback can begin. If playback speed exceeds the data transfer speed, playback may be paused for a period of time while additional data is downloaded, interrupting the viewing experience. However, the video playback quality may be higher when the playback occurs because of the potentially higher data rate. For example, if a 100 Kbps video file can be delivered over a 56 kbps modem, the video will be presented at the 100 kbps rate, but there may be periods when playback will be paused while additional video data is downloaded. The video data is typically downloaded and stored as a temporary file in its entirety.
Web servers typically use HTTP (hypertext transport protocol) on top of TCP (transfer control protocol) to transfer files over the network. TCP, which controls the transport of data packets over the network, is optimized for guaranteed delivery of data, not speed. Therefore, if a browser senses that data is missing, a resend request will be issued and the data will be resent. In networks with high delivery errors, resend requests may consume a large amount of bandwidth. Since TCP is not designed for efficient delivery of adequate data or bandwidth control (but rather guaranteed delivery of all data), it is not preferred for the delivery of video data in all applications.
Streaming delivers media content continuously to a media player and media playback occurs simultaneously. The end-user is capable of playing the media immediately upon delivery by the content provider. Traditional streaming techniques originate from a single provider delivering a stream of data to a set of end-users. High bandwidths and central processing unit (CPU) power are required to deliver a single stream to a large audience, and the required bandwidth of the provider increases as the number of end-users increases.
Unlike progressive downloading, streaming media can be delivered on-demand or live. Wherein progressive download requires downloading the entire file or downloading enough of the entire file to start playback at the beginning, streaming enables immediate playback at any point within the file. End-users may skip through the media file to start playback or change playback to any point in the media file. Hence, the end-user does not need to wait for the file to progressively download. Typically, streaming media is delivered from a few dedicated servers having high bandwidth capabilities.
A streaming media server is a specialized device that accepts requests for video files, and with information about the format, bandwidth and structure of those files, delivers just the amount of data necessary to play the video, at the rate needed to play it. Streaming media servers may also account for the transmission bandwidth and capabilities of the media player. Unlike the web server, the streaming media server communicates with the user computer using control messages and data messages to adjust to changing network conditions as the video is played. These control messages can include commands for trick play functions such as fast forward, fast reverse, pausing, or seeking to a particular part of the file. Since a streaming media server transmits video data only as needed and at the rate that is needed, precise control over the number of streams served can be maintained. Unlike the case with progressive downloading, the viewer will not be able to view high data rate videos over a lower data rate transmission medium. However, streaming media servers (1) provide users random access to the video file, (2) allows monitoring of who is viewing what video programs and how long they are watched (3) use transmission bandwidth more efficiently, since only the amount of data required to support the viewing experience is transmitted, and (4) the video file is not stored in the viewer's computer, but discarded by the media player, thus allowing more control over the content.
Streaming media servers may use HTTP and TCP to deliver video streams, but generally use RSTP (real time streaming protocol) and UDP (user datagram protocol). These protocols permit control messages and save bandwidth by reducing overhead. Unlike TCP, when data is dropped during transmission, UDP does not transmit resent requests. Instead, the server continues to send data. Streaming media servers can also deliver live webcasts and can multicast, which allows more than one client to tune into a single stream, thus saving bandwidth.
The foregoing technologies permit a broad spectrum of media programs to be made available to the user for immediate viewing. One of the challenges in providing such a broad array of media programs is that it is difficult for the user to find programs of interest from among the many programs available. Interfaces can be provided that place media programs into different categories that can be searched by the user, however such interfaces are only useful if the user already has an idea about what kind of media program they are interested in. Offbeat, unusual, or difficult to categorize media programs, for example, would be difficult to find with such interfaces.
One solution to this problem is to use a recommendation engine to recommend one or more programs to a user. Such recommendation engines can generate recommended media programs based on a number of possible factors, including how close one particular media program is related to another media program in such a way so that it may be inferred that if the user likes one program, it is more likely than average that they will like a second program.
One method for determining if two media programs are related as described above is to employ user feedback regarding media programs that they have been exposed to. Such user feedback may be direct, indirect, expressed, or inferred. For example, feedback may include watching the media program itself (a user that watches an entire media program is more likely than not to like the media program), expressly rating the media program (e.g. the user is expressly asked whether they enjoy the media program, and the response is recorded and used to recommend other media programs to the user or the same media program to other users), subscribing to a series of media programs (which provides an even stronger implication that the user likes the media programs in question), or queuing a media program. The notion is that the more users give similar feedback to two particular media program, the closer those two media programs are related, and the more appropriate it may be for to recommend one of those media programs to a user who has viewed or is viewing the other media program.
As described herein, different approaches have been devised to use user feedback to perform the task of recommending media programs, and such approaches work well in most circumstances. However, the weakness in all known approaches is that the popularity of the media programs involved can skew the results. For example, consider a case with a first media program (i) and a second media program (j). If media program (j) were very popular, media program (i) would be assumed to be related to media program (j) even if media program (i) and media program (j) had no apparent similarities. This occurs because so many users have positive feedback for media program (j) that at least part of those users will also like media program (i), regardless of how closely related the two media programs are. Thus, two completely unrelated media programs may be determined to be related to one another, and one of those media programs improvidently recommended to a user.
What is needed is a more accurate approach for measuring the relatedness of media programs that can be effectively used to recommend a second media program to a user who enjoys a first media program. The present invention satisfies this need.